How Meta’s TRIBE v2 Predicts Human Brain Activity Using AI
720 subjects. 1,115 hours of brain scans. One trimodal AI model that simulates 30 years of controlled neuroscience experiments without booking a single scanner session. Image by DALL-E The Problem That Took 50 Years to Name Imagine trying to understand a city by only ever reading reports from one neighbourhood at a time. The traffic report covers the roads. The housing report covers buildings. The cultural report covers theatres and restaurants. Each one is thorough and carefully researched. But nobody has ever put them together into a single map that shows how the city actually functions as a living whole. That is almost exactly how neuroscience has worked since the 1950s. One laboratory spends decades mapping how the brain processes faces. Another map's language. A third spends years on
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
arXiv:2512.16284v2 Announce Type: replace Abstract: Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for practitioners to evaluate and benchmark the degree of privacy protection offered by synthetic data. In this paper, we propose a framework to empirically assess the efficacy of tabular synthetic data privacy quantification methods through controlled, deliberate risk insertion. To demonstrate this framework, we survey existing approaches to synthetic data privacy quantification and the relate
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
arXiv:2512.16284v2 Announce Type: replace Abstract: Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for practitioners to evaluate and benchmark the degree of privacy protection offered by synthetic data. In this paper, we propose a framework to empirically assess the efficacy of tabular synthetic data privacy quantification methods through controlled, deliberate risk insertion. To demonstrate this framework, we survey existing approaches to synthetic data privacy quantification and the relate
A technical, 100% local writeup on how I replicated and then surpassed the Secret Detection model from Wiz (and the challenges along the way) - including labeling an entire dataset with local AI
Hey everybody, I have a strong interest in offloading work to small, specialized models that I can parallelize - this lets me scale work significantly (plus, I am less dependent on proprietary APIs) Some time ago, I saw a blog post from Wiz about fine-tuning Llama 3.2-1B for secret detection in code. They got 86% Precision and 82% Recall. I wanted to see if I can replicate (or beat) those numbers using purely local AI and produce a local specialized model. After a couple of weekends of trying it out I managed to get a Llama 3.2-1B hitting 88% Precision and 84.4% Recall simultaneously! I also benchmarked Qwen 3.5-2B and 4B - expectedly, they outperformed Llama 1B at the cost of more VRAM and longer inference time. I’ve put together a full write-up with the training stats, examples, and a st
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